ABSTRACT

Historical maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more time-consuming and labor-intensive to conduct an analysis that requires a synthesis of the information from multiple historical maps. To facilitate the use of the geographic information contained in historical maps, one way is to build a geographic knowledge graph (GKG) from them. This paper proposes a general workflow for completing one important step of building such a GKG, namely aligning the same geographic entities from different maps. We present this workflow and the related methods for implementation, and systematically evaluate their performances using two different datasets of historical maps. The evaluation results show that machine learning and deep learning models for matching place names are sensitive to the thresholds learned from the training data, and a combination of measures based on string similarity, spatial distance, and approximate topological relation achieves the best performance with an average F-score of 0.89.

摘要

历史地图包含有关一个地区过去的丰富地理信息。在数字地图出现之前，它们有时是唯一的信息来源。尽管它们的内容很有价值，但由于其纸质地图或扫描图像的形式，访问和使用历史地图中的信息通常具有挑战性。进行需要综合来自多个历史地图的信息的分析甚至更耗时耗力。为了方便使用历史地图中包含的地理信息，一种方法是从它们构建地理知识图（GKG）。本文提出了完成构建此类 GKG 的一个重要步骤的一般工作流程，即对齐来自不同地图的相同地理实体。我们介绍了这个工作流程和相关的实现方法，并使用两个不同的历史地图数据集系统地评估了它们的性能。评估结果表明，匹配地名的机器学习和深度学习模型对从训练数据中学习到的阈值很敏感，基于字符串相似性、空间距离和近似拓扑关系的度量组合达到了最佳性能，平均F 分数为 0.89。